Research

Health disparities are, in some dimensions, a problem of information access. My research sits at the intersection of artificial intelligence, human computer interaction and education to advance cancer health equity through purposeful information presentation: That is, every resource should provide a unique piece of information that is potentially useful and on-point with a particular topic or event. Check out these projects:

Mi Guia

An application to help breast cancer survivors improve their health and quality of life.

Persuasion through Dialogue

Trying to understand how perception works and how can we persuade people to take healthy actions thorugh an intelligent dialogue system

Evaluating Topic Models

This project aims to find an evaluation method for topic models that correlates with human judgements of topics that make sense

Automatic Personality Detection

Understanding how personality is expressed and perceived through textual data

Social Tools to Learn Computer Programming

We are exploring educational tools to help computer science students overcome the learning curve of programming.

Publications

Here's a list of peer reviewed publications

About

I am an associate professor in the Computer Science Department at Northeastern Illinois University. I usually am at LWH (Lech Walesa Hall) 3060. Check my classes and office hours on this calendar. My email is f&dash;iacobelli@neiu.edu
I got my undergraduate degree in Systems Engineer and Informatics at Universidad Diego Portales in Santiago, Chile. A masters degree in Computer Science at DePaul University with a concentration in AI; I got a Ph.D. in Computer Science at the Infolab, in the Computer Science department of Northwestern University. Other interests include: Computational linguistics and enhancing social interactions with technology that can help make a difference, such as intelligent tutoring systems and other aspects of AI applied to the education of children. In the past I worked developing Virtual Peers for minority children with Justine Cassell at the ArticuLab.
Here's my CV

Software

(swipe to see more)

Latent Dirichlet Allocation (LDA)

If we assume documents are comprised of different topics, then we can assume that certain words belong to certain topics. Therefore, a document is a set of words that are more or less grouped according to their topic. For example: A sports story can have a few topics in it such as "the actual game" (score, plays, etc.), "the players" (injuries, drug testing, etc.) and "speculation" (what will happen against the next rival). For each of these topics there are words that are strongly associated with them. For example: "score" is strongly associated to "the actual game" topic, while "steroids" is probably associated to "the players" topic. However, "score" may also be associated with the "speculation" topic albeit less strongly.

LDA attempts to group these words automatically from large collections fo text. Github Repo.

Other Simple Scripts

I have written a bunch of simpler scripts to emulate argparse in Java, small unit testing software for grading java assignments, models to convert simple beamer (latex presentations) into Reveal.js, a student appointment system, etc. these are my pet projects.